Search Results (358)

Unmanned aerial vehicle (UAV) clusters are increasingly deployed in power system applications, such as transmission line inspection, fault diagnosis, and post-disaster emergency communication restoration. Nonetheless, limitations of range and battery capacity have rendered the assurance of uninterrupted task operation a critical concern. Efficient cooperation and energy replenishment solutions are crucial for effective UAV cluster scheduling to resolve this issue. This study proposes an innovative scheduling method that integrates UAV path planning with laser-based remote charging technology. Initially, a scheduling model incorporating both energy consumption and task completion time is established. Subsequently, an integrated laser-powered UAV model is proposed, unifying charging operations with mission execution processes. Furthermore, a distributed robust optimization (DRO) framework is proposed to handle spatiotemporal uncertainties, particularly those caused by weather conditions. Finally, the proposed scheduling method is applied to a disaster recovery scenario of a power system. Simulation results demonstrate that the proposed strategy significantly outperforms traditional scheduling methods without remote charging by achieving higher task completion rates and improved energy efficiency. These findings substantiate the effectiveness and engineering feasibility of the proposed method in enhancing UAV cluster operational capabilities under stringent energy constraints. Full article

This study employs a systems theory approach to investigate the coupling coordination and driving mechanisms within the economic–social–environmental (ESE) system in China’s ethnic regions. It analyzes 67 ethnic counties in Sichuan Province, using an integrated framework that combines dynamic Shannon entropy, coupling coordination modeling, and GeoDetector. Based on data from 2005 to 2024, the study reveals the spatiotemporal patterns of ESE coupling coordination. The key findings are as follows: (1) The coupling coordination degree has gone through four stages: moderate imbalance → mild imbalance → primary coordination → moderate coordination. By 2024, 81.8% of counties had achieved coordinated development, and “highly coordinated” counties emerged for the first time. (2) The Western Sichuan Plateau has formed a high–high agglomeration zone by monetizing ecological assets and utilizing ethnic cultural resources. In contrast, the hilly and parallel ridge–valley regions in central and eastern Sichuan remain in low–low agglomerations due to their dependency on traditional industrialization paths. The decrease in high–low and low–high outliers indicates the recent policy polarization effects. (3) The interaction between habitat quality and per capita GDP has the strongest explanatory power. The rising marginal contributions of energy and carbon emission intensity suggest that green industrialization is crucial to breaking the “poverty–pollution” trap. Full article

Enhancing tourism eco-efficiency (TEE) is crucial for achieving China’s “dual carbon” objectives. This study examines nine provinces in the Yellow River Basin from 2010 to 2022, employing a super-efficiency SBM model, kernel density estimation, gravity center migration, standard deviation ellipse, Tobit regression, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate spatial-temporal variations and influencing factors. The results show that TEE increased steadily before 2019, declined during the COVID-19 pandemic, and recovered after 2021. Spatially, widening disparities and a polarization trend were observed, with the efficiency center remaining relatively stable in Shaanxi Province. Factors such as advancements in tourism economic development, regional economic growth, technological innovation, and infrastructure improvements significantly promote TEE, whereas stringent environmental regulations and greater openness exert constraints, and the impact of human capital remains uncertain. Four types of condition combinations were identified—economic-driven, market-innovation-driven, scale-innovation-driven, and balanced development. Managerial implications highlight the need for region-specific pathways and regional cooperation, with a dual focus on technological and institutional drivers as well as ecological value orientation, to sustainably enhance TEE in the Yellow River Basin. Full article

Ecosystem services play a vital role in human well-being, with land-use changes exerting substantial influence on ecosystem service value (ESV) and land-use carbon emissions (LUCEs). Understanding the spatio-temporal relationship and transition dynamics between ESV and LUCEs is essential for promoting high-quality ecological development aligned with the “dual carbon” objective. This study takes the Loess Plateau as the research object. Based on five-phase land-use data from 2000 to 2020, the ESV and LUCEs are calculated. Exploratory spatio-temporal data analysis is used to explore their spatio-temporal relationship and transition paths, and the quadrant model is introduced to analyze the transition patterns from the perspective of ecological quality. The results indicate the following: (1) From 2000 to 2020, the ESV of the Loess Plateau increased from CNY 579.032 billion to CNY 582.470 billion, with an overall increase of only 0.15%. Among the changes in land use, changes in forest and grassland significantly affected the ESV. (2) The LUCEs from land use on the Loess Plateau increased from 137.15 Mt to 458.43 Mt, with an average annual growth rate of 6.22%. Affected by industrialization and urbanization, the LUCEs showed significant spatial differences at the provincial and county scales. (3) There was a certain positive spatial correlation between ESV and LUCEs. The distribution of significantly correlated areas did not change significantly from 2000 to 2020, and the relationship characteristics were mainly characterized by Type IV transitions. (4) At the county scale, ESV and LUCEs exhibited temporal stability, with most counties situated in the general ecological category, indicating substantial potential for enhancing regional ecological quality. These research outcomes offer a foundational framework for devising tailored regional carbon emission reduction strategies. Full article

The frequent occurrence of sandstorms on the Mongolian Plateau has become a critical factor influencing the stability of regional ecosystems and social activities. In this study, a deep learning framework was developed for predicting sandstorm paths on the Mongolian Plateau. A spatio-temporal feature dataset was established using remote sensing imagery and meteorological observations. Spatial features were extracted through a convolutional neural network (CNN), while the temporal evolution of sandstorms was modeled using a bidirectional long short-term memory (BiLSTM) network. A random forest algorithm was employed to assess the relative importance of meteorological and geographical factors. The results indicate that the proposed CNN-BiLSTM model achieved strong performance at prediction intervals of 1, 6, 12, 18, and 24 h, with overall accuracy, F1-score, and AUC all exceeding 0.80. The 24 h prediction yielded the best results, with evaluation metrics of 0.861, 0.878, and 0.898, respectively. Compared with the individual CNN and BiLSTM models, the CNN-BiLSTM model demonstrated superior performance. The findings suggest that the model provides high predictive accuracy and stability across different time steps, thereby offering strong support for dust storm path prediction on the Mongolian Plateau and contributing to the reduction of disaster-related risks and losses. Full article

Oases are the core carriers of societal development in arid regions, and their spatial patterns have changed significantly, driven by climate change and anthropogenic activities. This study integrates historical documents, archeological materials, maps, and remote sensing data. The changes in the temperature, precipitation, settlements, war frequency, and oasis area were identified by combining quantitative and qualitative methods, and the partial least squares path model (PLS-PM) was utilized to quantify the natural and human driving factors. The results show that the oasis development in the Tarim and Heihe River Basins exhibits distinct spatio-temporal variability and phased characteristics and is comprehensively shaped by both natural and anthropogenic drivers. The Tarim Basin’s natural oases demonstrate a “fluctuating recovery” pattern. The cultivated oases gradually expanded. The natural oases within the Heihe River Basin have persistently decreased, and cultivated oases show a “U”-shaped evolution pattern. This reflects the strong intervention of human reclamation in the cultivated oases. The introverted social ecosystem has endowed the Tarim River Basin with the ability to self-repair and achieve a periodic recovery. The Heihe River Basin serves as a strategic corridor for national external engagement, relying on regime stability. A regime collapse led to its lack of a stable recovery period. The PLS-PM reveals that the Tarim River Basin oasis evolution is predominantly driven by climate fluctuations. The path coefficient of natural factors for artificial oases is 0.63, and extreme drought leads to natural oasis contraction. The human influence dominates the Heihe River Basin, with a −0.93 path coefficient linking the cultivated oasis area to human factors. The frequency of wars (load 0.74) and changes in settlements (load −0.92) are the key factors. This study provides a powerful case for the analysis of the evolution and driving mechanism of future oases in drylands. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

Desertification poses critical threats to agricultural productivity and socio-economic stability, particularly in vulnerable regions like Thatta and Badin districts of Sindh, Pakistan. Traditional monitoring methods lack the accuracy and temporal resolution needed for effective early warning systems. This study presents a novel Spatio-Temporal Desertification Predictor (STDP) framework that integrates deep learning with next-generation satellite imaging for time-series desertification forecasting. The proposed encoder–decoder architecture combines Convolutional Neural Networks (CNNs) for spatial feature extraction from high-resolution satellite imagery with modified Long Short-Term Memory (LSTM) networks enhanced by multi-head attention to capture temporal dependencies. Environmental variables are fused through an adaptive data integration layer, and hyperparameter optimization is employed to enhance model performance for edge computing deployment. Experimental validation on a 15-year satellite dataset (2010–2024) demonstrates superior performance with MSE = 0.018, MAE = 0.079, and $R^2=0.94$, outperforming traditional CNN-only, LSTM-only, and hybrid baselines by 15–20% in prediction accuracy. The framework forecasts desertification trends through 2030, providing actionable signals for environmental management and policy-making. This work advances the integration of AI with satellite-based Earth observation, offering a scalable path for real-time environmental monitoring in IoT and edge computing infrastructures. Full article

This study investigates the spatiotemporal optimization of small-scale emergency infrastructure in high-density urban environments, using nucleic acid testing sites in Macao as a case study. The objective is to enhance emergency responsiveness during future public health crises by aligning infrastructure deployment with dynamic patterns of population influx. A behaviorally informed spatial decision-making framework is developed through the integration of kernel density estimation, point-of-interest (POI) distribution, and origin–destination (OD) path simulation based on an Ant Colony Optimization (ACO) algorithm. The results reveal pronounced temporal fluctuations in testing demand—most notably with crowd peaks occurring around 12:00 and 18:00—and highlight spatial mismatches between existing facility locations and key residential or functional clusters. The proposed approach illustrates the feasibility of coupling infrastructure layout with real-time mobility behavior and offers transferable insights for emergency planning in compact urban settings. Full article

The Sanniang Bay (SNB) and Dafeng River Estuary (DFR) in the Northern Beibu Gulf, China, are critical habitats for the Indo-Pacific humpback dolphin (Sousa chinensis). However, whether and how the decreased dissolved oxygen (DO) has happened in bottom seawater remains poorly understood. This study investigated DO depletion and microbial community responses using a multidisciplinary approach. High-resolution spatiotemporal sampling (16 stations across four seasons) was combined with functional annotation of prokaryotic taxa (FAPROTAX) to characterize anaerobic metabolic pathways and quantitative PCR (qPCR) targeting dsrA and dsrB genes to quantify sulfate-reducing bacteria. Partial least-squares path modeling (PLS-PM) was employed to statistically link environmental variables (seawater properties and nutrients) to microbial community structure. Results revealed pronounced bottom DO declining to 5.44 and 7.09 mg L⁻¹, a level approaching sub-optimal state (4.0–4.8 mg L⁻¹) in September. Elevated chlorophyll-a (Chl-a) near the SDH coincided with anaerobic microbial enrichment, including sulfate reducers (dsrA/dsrB abundance: SNB > DFR). PLS-PM identified seawater properties (turbidity, DO, pH) and nitrogen as key drivers of anaerobic taxa distribution. Co-occurrence network analysis further demonstrated distinct microbial modules in SNB (phytoplankton-associated denitrifiers) and DFR (autotrophic sulfur oxidizers, nitrogen fixation, and denitrification). These findings highlight how environmental factors drive decreased DO, reshaping microbial networks and threatening coastal ecosystems. This work underscores the need for regulating aquaculture/agricultural runoff to limit eutrophication-driven hypoxia and temporarily restrict human activities in SNB during peak hypoxia (September–October). Full article

With the rapid growth of urban logistics demand, carbon emissions and the time-varying nature of vehicle speeds have become critical challenges in sustainable transportation planning. This paper addresses a Time-Dependent Green Vehicle Routing Problem (TDGVRP) that integrates time-varying speeds, carbon emissions, and cold chain logistics under a joint distribution framework involving multiple depots and homogeneous refrigerated vehicles. A Mixed-Integer Linear Programming (MILP) model is developed, explicitly considering carbon pricing, refrigeration energy consumption, and speed variations across different time periods. To efficiently solve large-scale instances, a Three-Phase Heuristic (TPH) algorithm is proposed, combining spatiotemporal path construction, local-improvement strategies, and an Adaptive Large Neighborhood Search (ALNS) mechanism. Computational experiments show that the proposed method outperforms traditional Genetic Algorithms (GAs) in both solution quality and computation time, and in some benchmark cases even achieves better results than the commercial solver Gurobi, demonstrating its robustness and scalability. Using real-world traffic speed data, comparative analysis reveals that the joint distribution strategy reduces total logistics costs by 14.40%, carbon emission costs by 23.12%, and fleet size by approximately 25% compared to single-entity distribution. The findings provide a practical and scalable solution framework for sustainable cold chain logistics routing in time-dependent urban road networks. Full article

As China’s urbanization deepens, the spatial structure of residential areas and land use patterns has undergone profound transformations, with the differentiation of housing prices emerging as a key indicator of urban spatial dynamics and socioeconomic stratification. This study examines the spatial and temporal evolution of residential housing prices in Qingdao’s main urban area over a 20-year period, using data from three representative years (2003, 2013, and 2023) to capture key stages of change. It employs Local Indicators of Spatial Association (LISA) spatial and temporal path and leap analyses, as well as Geographically and Temporally Weighted Regression (GTWR) modeling. The results show that Qingdao’s housing price patterns exhibit distinct spatiotemporal heterogeneity, characterized by multi-level transitions, leapfrog dynamics and strong spatial dependence. The urban center and coastal zones demonstrate positive synergistic growth, while some inland and peripheral areas show negative spatial coupling. Evident is the spatial restructuring from a monocentric to a polycentric pattern, driven by shifts in industrial layout, policy incentives, and transportation infrastructure. Key driving factors, such as community attributes, locational conditions, and amenity support, show differentiated impacts across regions and over time. Business agglomeration and educational resources are primary positive drivers in central districts, whereas natural environments and commercial density play a more complex role in peripheral areas. These findings provide empirical evidence to inform our understanding of housing market dynamics and offer insights into urban planning and the design of equitable policies in transitional urban systems. Full article

Landslides pose persistent threats to mountainous regions in Taiwan, particularly in areas such as Nanfeng Village, Nantou County, where steep terrain and concentrated rainfall contribute to chronic slope instability. This study investigates spatiotemporal patterns of vegetation change as a proxy for identifying potential landslide-prone zones, with a focus on the Tung-An tribal settlement in the eastern part of the village. Using high-resolution satellite imagery from SPOT 6/7 (2013–2023) and Pléiades (2019–2023), we derived annual NDVI layers to monitor vegetation dynamics across the landscape. Long-term vegetation trends were evaluated using the Mann–Kendall test, while spatiotemporal clustering was assessed through Emerging Hot Spot Analysis (EHSA) based on the Getis-Ord Gi* statistic within a space-time cube framework. The results revealed statistically significant NDVI increases in many valley-bottom and mid-slope regions, particularly where natural regeneration or reduced disturbance occurred. However, other valley-bottom zones—especially those affected by recurring debris flows—still exhibited declining or persistently low vegetation. In contrast, persistent low or declining NDVI values were observed along steep slopes and debris-flow-prone channels, such as the Nanshan and Mei Creeks. These zones consistently overlapped with known landslide paths and cold spot clusters, confirming their ecological vulnerability and geomorphic risk. This study demonstrates that integrating NDVI trend analysis with spatiotemporal hot spot classification provides a robust, scalable approach for identifying slope hazard areas in data-scarce mountainous regions. The methodology offers practical insights for ecological monitoring, early warning systems, and disaster risk management in Taiwan and other typhoon-affected environments. By highlighting specific locations where vegetation decline aligns with landslide risk, the findings can guide local authorities in prioritizing slope stabilization, habitat conservation, and land-use planning. Such targeted actions support the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by reducing disaster risk, enhancing community resilience, and promoting the long-term sustainability of mountain ecosystems. Full article

River flow forecasting remains a critical yet challenging task in hydrological science, owing to the inherent trade-offs between physics-based models and data-driven methods. While physics-based models offer interpretability and process-based insights, they often struggle with real-world complexity and adaptability. Conversely, purely data-driven models, though powerful in capturing data patterns, lack physical grounding and often underperform in extreme scenarios. To address this gap, we propose PESTGCN, a Physical-Enhanced Spatio-Temporal Graph Convolutional Network that integrates hydrological domain knowledge with the flexibility of graph-based learning. PESTGCN models the watershed system as a Heterogeneous Information Network (HIN), capturing various physical entities (e.g., gauge stations, rainfall stations, reservoirs) and their diverse interactions (e.g., spatial proximity, rainfall influence, and regulation effects) within a unified graph structure. To better capture the latent semantics, meta-path-based encoding is employed to model higher-order relationships. Furthermore, a hybrid attention mechanism incorporating both local temporal features and global spatial dependencies enables comprehensive sequence learning. Importantly, key variables from the HEC-HMS hydrological model are embedded into the framework to improve physical interpretability and generalization. Experimental results on four real-world benchmark watersheds demonstrate that PESTGCN achieves statistically significant improvements over existing state-of-the-art models, with relative reductions in MAE ranging from 5.3% to 13.6% across different forecast horizons. These results validate the effectiveness of combining physical priors with graph-based temporal modeling. Full article

The online coupling of aerosols and clouds and its effect on surface global horizontal irradiance (GHI) has not yet been thoroughly investigated in the Weather Research and Forecasting Model with Solar extensions (WRF-Solar), despite its potential significance for solar energy applications. This study addresses this critical gap by implementing a computationally efficient, coupled aerosol–cloud scheme and evaluating its impacts on GHI predictability. Simulations with online aerosol–cloud coupling are systematically compared to uncoupled simulations during March 2021, a period marked by two distinct pollution episodes over north China. The online coupling enhances aerosol optical depth (AOD) simulations, increasing the correlation coefficient from 0.19 to 0.51 while reducing the absolute bias from 0.54 to 0.48 and root mean square error from 0.82 to 0.72, compared to uncoupled simulations. Enhanced cloud microphysics (droplet concentration, water path) yields better cloud optical depth estimates, reducing all-sky GHI bias by 14.5% (63.5 W/m² for the uncoupled scenario and 54.3 W/m² for the coupled scenario) through improved aerosol–cloud–meteorology interactions. Notably, the simultaneous spatiotemporal improvement of both AOD and GHI suggests enhanced internal consistency in aerosol–cloud–radiation interactions, which is crucial for operational solar irradiance forecasting in pollution-prone regions. The results also highlight the practical value of incorporating online aerosol coupling in solar forecasting models. Full article